Artificial intelligence-powered medical technologies are rapidly evolving into clinically applicable solutions. Deep learning algorithms can handle increasing amounts of data from wearables, smartphones, and other mobile monitoring sensors in various areas of medicine. At the moment, only a few clinical settings benefit from the use of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or disease diagnosis based on histopathological examination or medical imaging. Patients have long awaited the implementation of augmented medicine because it allows for greater autonomy and more personalised treatment; however, it has been met with resistance from physicians who were not prepared for such a shift in clinical practise. This phenomenon also necessitates the need to validate these modern tools through traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine, and consider the ethical implications of continuous connected monitoring. The purpose of this paper is to review recent scientific literature and offer an opinion on the benefits, future opportunities, and risks of established artificial intelligence applications in clinical practise on physicians, healthcare institutions, medical education, and bioethics.
By incorporating AI into medical imaging, physicians can identify conditions much more quickly, allowing for earlier intervention.
Tulane University researchers discovered that AI can detect and diagnose colorectal cancer as well as or better than pathologists by analysing tissue scans.
The goal of this study was to see if artificial intelligence could be used to help pathologists keep up with the rising demand for their services.
Pathologists regularly evaluate and label thousands of histopathology images to determine whether a patient has cancer, according to the researchers. However, their average workload has increased significantly, which may result in unintentional misdiagnosis.
"Even though much of their work is repetitive, most pathologists are extremely busy because there is a huge demand for what they do, but there is a global shortage of qualified pathologists, especially in many developing countries," said Hong-Wen Deng, PhD, Professor and Director of the Tulane Center of Biomedical Informatics and Genomics at Tulane University School of Medicine, in a press release.
"This study is groundbreaking because we successfully used artificial intelligence to identify and diagnose colorectal cancer in a cost-effective manner, which could ultimately reduce pathologists' workload."
Measuring various heart structures can help determine a patient's risk of cardiovascular disease. Furthermore, automating the detection of abnormalities in imaging tests can result in faster decision-making and fewer diagnostic errors.
AI can identify left atrial enlargement from chest x-rays to rule out other cardiac or pulmonary problems, assisting providers in directing patients to the most appropriate treatments.
Other measurement tasks, such as aortic valve analysis, carina angle measurement, and pulmonary artery diameter, could be automated using similar AI tools.
AI applied to imaging data could also aid in identifying
muscle thickening or monitoring changes in blood flow through the heart and
associated arteries. AI has the potential to detect cancerous lesions.
Artificial intelligence in medicine market can also detect fractures, diagnose neurological diseases, and identify thoracic complications.
AI can also be used in medical imaging to improve precision medicine. For example, researchers at Stanford University discovered that a machine learning tool could distinguish between two types of lung cancer.
Furthermore, the machine learning tool outperformed pathologists' standard approach of classifying tumours by grade and stage in predicting patient survival rates.
"Pathology as it is currently practised is very subjective," said Michael Snyder, PhD, professor and chair of genetics, in a press release.
"Only about 60% of the time will two highly skilled pathologists evaluating the same slide agree." This approach eliminates subjectivity in favour of sophisticated, quantitative measurements that we believe will improve patient outcomes."
The use of AI removes the subjective element from the equation. The tool can determine the type of cancer and the best course of treatment for the patient, thereby advancing precision medicine efforts. Physicians can use precision medicine to provide a personalised treatment approach that specifically targets the illness.
While AI in medical imaging can be used to identify current conditions affecting a patient, it can also predict the risk of future illnesses.
Researchers discovered that by combining AI imaging techniques with clinical data, physicians could improve predictive models indicating a patient's risk for heart attacks in a recent study.
Researchers discovered that coronary 18F-NaF uptake on PET and quantitative coronary plaque characteristics on CT angiography were complementary and robust predictors of heart attack risk in patients with established coronary artery disease when analysed together in an artificial intelligence model.
When used together, the two techniques could provide a more accurate prediction of heart attack risk than clinical data alone.
"Recently, advanced imaging techniques have shown significant promise in determining which coronary artery disease patients are most likely to have a heart attack." "These techniques include 18F-sodium fluoride (18F-NaF) PET, which assesses disease activity in the coronary arteries, and CT angiography, which provides a quantitative plaque analysis," said Piotr J. Slomka, PhD, FACC, FASNC, FCCPM, Director of Innovation in Imaging at Cedars-Sinai Medical Center, in a press release.
"The study's goal was to see if the information provided by 18F-NaF PET and CT angiography was complementary and could improve prediction of heart attacks using artificial intelligence techniques."
The findings supported the use of artificial intelligence methods for integrating multimodality imaging and clinical data for accurately predicting heart attacks, according to the research team.
Machine learning methods, when combined with AI, can advance medical screenings, improve precision medicine, analyse patient risk factors, and lighten the load on physicians.